Q-omics provides the consensus-scored C16orf72 profile across patient tissues and cancer cell-line models. C16orf72 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, C16orf72 is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, C16orf72 RNA expression shows 20,816 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, HNSC, and ACC as cancer lineages where C16orf72 shows reproducible signals across survival, tumor–normal expression, and patient cross-omics analyses.
Every result is evaluated using two consensus scores. Sampling consensus measures how consistently a finding is reproduced within a cancer lineage across different conditions. Lineage consensus measures how broadly the result is shared across cancer types, distinguishing pan-cancer signals from lineage-specific patterns.
Premium analyses for C16orf72 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes C16orf72 survival associations across molecular data types. C16orf72 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (4) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible C16orf72 RNA expression–survival associations across cancer types. High C16orf72 expression shows unfavorable associations in BLCA, HNSC, LUSC and UVM, but favorable associations in KIRC and COAD. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for C16orf72 RNA expression.
This table summarizes C16orf72 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 2. The strongest signals are observed in HNSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for C16orf72. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. C16orf72 shows lower tumor expression in THCA and KICH and higher tumor expression in HNSC, LIHC, BRCA and CHOL. The HNSC box plot shows higher C16orf72 RNA expression in tumor versus normal tissue (log2 FC = +0.998, t-test p < 0.001).
This table shows molecular features associated with C16orf72 in patient tissues and cancer cell lines. In patient samples, C16orf72 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, C16orf72 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LARGE_INTESTINE.
